Active learning (AL) is a machine learning technique that aims to reduce annotation costs by selectively choosing the most informative samples for labeling. This process relies on acquisition functions, which can be broadly categorized into two types: representativity-based and uncertainty-based. Representativity-based functions focus on exploring the dataset, while uncertainty-based functions refine decision boundaries. This creates a trade-off known as the exploration-exploitation dilemma. To address this challenge, we propose a novel approach that alternates between these two types of acquisition functions. Our method employs an adaptive feedback-driven selection mechanism, an annealing-based approach, or a baseline random criterion to guide the alternation process. This strategy helps mitigate common AL issues, such as batch mode inefficiency and cold start problems. Our experiments demonstrate that the alternating approach enhances both the accuracy and robustness of the AL process. Additionally, we consider the balance between accuracy and energy consumption, contributing to the development of more sustainable AI systems. By evaluating our criterion across various models and datasets, we show its potential to reduce computational costs while maintaining or even improving accuracy. Notably, alternating between the BALD and BADGE acquisition functions yields particularly robust results.

Active learning (AL) is a machine learning (ML) approach that entails carefully choosing the most informative samples for annotation during training, aiming to minimize annotation costs. AL has recently emerged as a promising approach in the context of green ML, as an energy-efficient learning method on top of being data-efficient. Nevertheless, given the significant cost of AL, it might lead one to question the effectiveness of this approach in reducing computational costs and promoting green ML. In this paper, we conduct a comparative analysis of both fundamental and advanced active learning methods against a random baseline selection, aiming to demonstrate the efficacy of active learning to reduce the cost of training. This study demonstrates that, with careful tuning of hyperparameters like query size and pool size, AL is able to reduce runtime while maintaining competitive accuracy for classification tasks.